totals-modeling

To model NHL game total goals for over/under betting, integrate pace, special teams, goalie matchups, and contextual features to forecast scoring distributions and compare predicted probabilities against market odds.

Predicting NHL scoring distributions and identifying betting edges requires game results, team and goalie statistics, and market odds data to calibrate models and detect market inefficiencies in total goals.

Pace and goalie matchup analysis for hockey totals prediction works by integrating team pace and goalie stats with special teams data to optimize betting strategies and forecast expected total goals.

Yes, you can use pandas and scipy to process NHL game results and stats, building calibrated models that compare predicted total goals probabilities with market odds to find over/under value opportunities.

The best way to calculate betting edges for NHL totals using xgboost is training models on pace, special teams, and goalie matchup features to generate probabilistic predictions and evaluate over/under lines.

Limitations when predicting NHL over/under lines with contextual features include the necessity of comprehensive game results and market odds data for calibration, without which edge detection and market inefficiency evaluation may be inaccurate.